The Ethiopia Rural Household Survey (ERHS) is a unique longitudinal household data set covering households in a number of villages in rural Ethiopia. Data collection started in 1989, when a team visited 6 farming villages in Central and Southern Ethiopia. In 1989, IFPRI conducted a survey in seven Peasant Associations located in the regions Amhara, Oromiya and the Southern Ethiopian People’s Association (SNNPR). Civil conflict prevented survey work from being undertaken in Tigray. Under extremely difficult field conditions, household data were collected in order to study the response of households to food crises. The study collected consumption, asset and income data on about 450 households. In 1994, the survey was expanded to cover 15 villages across the country. An additional round was conducted in late 1994, with further rounds in 1995, 1997, 1999, 2004, and 2009. In addition, nine new villages were selected giving a sample of 1477 households. The nine additional communities were selected to account for the diversity in the farming systems in the country, including the grain-plough areas of the Northern and Central highlands, the enset-growing areas and the sorghum-hoe areas. Topics addressed in the survey include household characteristics, agriculture and livestock information, food consumption, health, women’s activities, as well as community level data on electricity and water, sewage and toilet facilities, health services, education, NGO activity, migration, wages, and production and marketing.

Abstract

The Ethiopian Rural Socioeconomic Survey (ERSS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study- Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic panel household level data with a special focus on improving agriculture statistics and the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

The specific objectives of the ERSS are: - Development of an innovative model for collecting agricultural data in conjunction with household data; - Strengthening the capacity to generate a sustainable system for producing accurate and timely information on agricultural households in Ethiopia; - Development of a model of inter-institutional collaboration between the CSA and relevant federal and local government agencies as well as national and international research and development partners; and - Comprehensive analysis of household income, well-being, and socio-economic characteristics of households in rural areas and small towns.

Geographic coverage

Rural and small towns

Analysis unit

• Household
• Person/ individual
• Community

Sampling procedure

The ERSS sample is designed to be representative of rural and small town areas of Ethiopia. The ERSS rural sample is a sub-sample of the AgSS while the small town sample comes from the universe of small town EAs. The ERSS sample size provides estimates at the national level for rural and small town households. At the regional level, it provides estimates for four regions including Amhara, Oromiya, SNNP, and Tigray.

The sample is a two-stage probability sample. The first stage of sampling entailed selecting primary sampling units , which are a sample of the CSA enumeration areas (EAs). For the rural sample, 290 EAs were selected from the AgSS EAs. The AgSS EAs were selected based on probability proportional to size of the total EAs in each region. For small town EAs, a total of 43 EAs were selected. In order to ensure sufficient sample in the most populous regions (Amhara, Oromiya, SNNP, and Tigray), quotas were set for the number of EAs in each region. The sample is not representative for each of the small regions including Afar, Benshangul Gumuz, Dire Dawa, Gambella, Harari, and Somalie regions. However, estimates can be produced for a combination of all smaller regions as one "other region" category.

The second stage of sampling was the selection of households to be interviewed in each EA. For rural EAs, a total of 12 households are sampled in each EA. Of these, 10 households were randomly selected from the sample of 30 AgSS households. The AgSS households are households which are involved in farming or livestock activities. Another 2 households were randomly selected from all other households in the rural EA (those not involved in agriculture or livestock). In some EAs, there is only one or no such households, in which case, less than two non-agricultural households were surveyed and more agricultural households were interviewed instead so that the total number of households per EA remains the same.

In the small town EAs, 12 households are selected randomly from the listing of each EA, with no stratification as to whether the household is engaged in agriculture/livestock. Households were not selected using replacement. Thus, the final number of household interviewed was slightly less than the 3,996 as planned in the design.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was administered using five questionnaires: household, community, post-planting agriculture, ost-harvest agriculture and livestock questionnaires.

The household questionnaire collects information on basic demographics; education; health (including anthropometric measurement for children); labor and time use; partial food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; credit; and other sources of household income. The household questionnaire, when relevant, is comparable to the Welfare Monitoring Survey (WMS).

The community questionnaire gathered information on access to infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Post-planting and post-harvest agriculture questionnaires were completed in those households with at least one member of the household engaged in crop farming using owned or rented land The post-planting and post-harvest agriculture questionnaires focused on farming activities and solicit information on land ownership and use; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization.

The livestock questionnaire interviews were implemented in households where at least one member was engaged in livestock rearing. The livestock questionnaire collected information on animal holdings and costs; and production, cost and sales of livestock byproducts.

Cleaning operations

Most of the interviews were carried out using paper and pen interviewing method. The completed paper questionnaires were sent to the CSA headquarters in Addis Ababa. The questionnaires were first checked by editors for completeness and consistency. The editors checked completeness (taking inventory) and cross-checked the questionnaires with the EA codebook. Questionnaires with inconsistent responses or with errors were corrected by contacting the branch offices or, in some cases, by sending the questionnaires back to the field. Checked questionnaires were keyed by data entry clerks at the head office using CSPro data entry application software.

Computer assisted personal interviewing (CAPI) was implemented, as a pilot, in 33 of the 333 EAs using SurveyBe data collection software.

The data cleaning process was done in two stages. The first step was at the CSA head office using the CSA's data cleaning staff. The CSA data cleaning staff used the CSpro data cleaning application to capture out of range values, outliers, and skip inconsistencies from the batch error reports. Once the errors were flagged in the batch error report the hard copy of the original questionnaire was retrieved and checked if the errors were at the data collection, editing, or entry level. Editing and entry level errors were corrected at the head office. Field level errors were communicated with the branch offices in the regions. The second level of data cleaning was done using Stata program to check for inconsistencies.

Response rate

A total of 3,969 households were interviewed with a response rate of 99.3 percent.

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.

The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.

The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).

Abstract

The Ethiopia Rural Household Survey (ERHS) is a unique longitudinal household data set covering households in a number of villages in rural Ethiopia. Data collection started in 1989, when a team visited 6 farming villages in Central and Southern Ethiopia. In 1989, IFPRI conducted a survey in seven Peasant Associations located in the regions Amhara, Oromiya and the Southern Ethiopian People’s Association (SNNPR). Civil conflict prevented survey work from being undertaken in Tigray. Under extremely difficult field conditions, household data were collected in order to study the response of households to food crises. The study collected consumption, asset and income data on about 450 households. In 1994, the survey was expanded to cover 15 villages across the country. An additional round was conducted in late 1994, with further rounds in 1995, 1997, 1999, 2004, and 2009. In addition, nine new villages were selected giving a sample of 1477 households. The nine additional communities were selected to account for the diversity in the farming systems in the country, including the grain-plough areas of the Northern and Central highlands, the enset-growing areas and the sorghum-hoe areas. T

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the survey include households and individuals

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

The ESS aims to collect multi-topic panel household-level data with a special focus on improving agriculture statistics and the link between agriculture and other household income activities in Ethiopia. The sample of ESS1 (formerly known as ERSS) included 4,000 households in rural areas that underwent baseline interviews from 2011 to 2012. This sample was followed up for ESS2 in 2013 to 2014 with the addition of 1,500 urban households in Addis Ababa, Ethiopia. All households were followed up in ESS3 from 2015 to 2016.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) was used as a frame to select EAs from the rural part of the country. On the other hand, the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC), was used as a frame in order to select sample enumeration areas for the urban HICE survey. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. This list was, thus, used as a frame in order to select households from sample EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of eight regional states and two administrative councils (Addis Ababa and Dire Dawa) of the country, except Gambella region. Each region was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Twelve households per sample EA were selected as a Second Stage Sampling Unit (SSU) to which the survey questionnaire were administered.

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata. Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living. The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were inally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of "other urban centers" is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three-stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Mode of data collection

Face-to-face [f2f]

These data are generated for the study conducted to evaluate a public extension program that integrates informational video screening with extension service provision to improve farmers’ knowledge and adoption of improved agricultural technologies and practices. Specifically, the study focuses on a program piloted by the Ethiopian Ministry of Agriculture (MoA), regional bureaus of agriculture, and Digital Green, a social enterprise, in the country’s four most agriculturally important regional states. Data for this study are drawn from a series of surveys conducted among farmers participating in the study. A survey of more than 2,400 randomly selected households assigned to one of the three treatment arms after the year 1 (2017) rollout in early 2018, following the meher season harvest. A subsequent round of household surveys was conducted in early 2019, following the year 2 (2018) rollout of the implementation. This dataset includes data from follow-up survey conducted in 2019. Data were collected using two separate questionnaires from both household heads and spouses. The household head questionnaire covered topics including household characteristics, assets, access to services, technology adoption, knowledge of agricultural practices, experience with video, crop sales, non-farm income, savings, food security, shocks, and plot-level information on land use, production, and inputs. The spouse questionnaire included sections on assets, technology adoption, knowledge of agricultural practices, and experience with video.

Data from two CIMMYT and KALRO household surveys representative of six maize production areas or agroecological zones in Kenya. The surveys were conducted in 2010 and 2013 collected data on farmer demographics, adoption of improved technologies and practices, marketing, access to agricultural information, and farmer adaptation to climate change.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 1999/2000 Household Income, Consurnption, and Expendi.ture Survey covered both the urban and the sedentary rural parts of the country. The survey has not covered six zones in Somalia Region and two zones in Afar Region that are inhabited mainly by nomadic population. For the purpose of the survey, the country was divided into three categories . That is, the rural parts of the country and the urban areas that were divided into two broad categories taking into account sizes of their population. Category I: Rural parts of nine Regional States and two administrative regions were grouped in this category each of which were the survey dornains (reporting levels). These regions are Tigrai,Afar, Amhara, Oromia, Sornalia, Eenishangul-Gunuz, SNNP,Gambela, Flarari, Addis Ababa and Dire Dawa.

Category II: All Regional capitals and five major urban centers of the country were grouped in this category. Each of the urban centers in this category was the survey domain (reporting level) for which separate survey results for rnajor survey characteristics were reported.

Category III: Urban centers in the country other than the urban centers in category II were grouped in this category and formed a single reporting level. Other than the reporting levels defined in category II and category III one additional domain, namely total urban (country level) can be constructed by eombining the basic domains defined in the two categories. All in all 35'basie rural and urban domains (reporting levels) were defined for the survey. In addition to the above urban and rural domains, survey results are to be reported at regional and eountry levels by aggregating the survey results for the conesponding urban and rural areas. Definition of the survey dornains was based on both technical and resource considerations. More specifically, sample size for the domains were determined to enable provision of major indicators with reasonable precision subject to the resources that were available for the survey.

Selection Scheme and Sample Size in Each Category CategoryI : A stratified two-stage sample design was used to select the sample in which the primary sampling units (PSUs) were EAs. Sample enumeration areas( EAs) from each domain were selected using systematic sampling that is probability proportional to the size being number of households obtained from the 1994 population and housing census.A total of 722 EAs were selected from the rural parts of the country. Within each sample EA a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the survey questionnaire 12 households per sample EA for rural areas were systematically selected.

Category II: In this category also,a stratified two-stage sample design was used to select the sample. Here a strata constitutes all the "Regional State Capitals" and the five "Major Urban Centers" in the country and are grouped as a strata in this category. The primary sampling units (PSUs) are the EA's in the Regional State Capitals and the five Major Urban Centers and excludes the special EAs (non-conventional households). Sample enumeration areas( EAs) from each strata were selected using systematic sampling probability proportional to size, size being number of households obtained from the 1994 population and housing census. A total of 373 EAs were selected from this domain of study. Within each sample EAs a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the questionnaire 16 household per sample EA were systematically selected-

Category III: Three-stage stratified sample design was adopted to select the sample from domains in category III. The PSUs were other urban centers selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. The secondary sampling units (SSUs) were EAs which were selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. A total of 169 sample EAs were selected from the sample of other urban centers and was determined by proportional allocation to their size of households from the 1994 census. Ultimately, 16 households within each of the sample EAs were selected systematically from a fresh list of households prepared at the beginning of the survey's fieldwork for the administration of the survey questionnaire.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Household Income, Consumption and Expenditure Survey questionnaire contains the following forms: - Form 1: Area Identification and Household Characteristics - Form 2A: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for first and second week. - Form 2B: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for third and fourth week . - Form 3A: All transaction (income, expenditure and consumption) for the first and second weeks except what is collected in Forms 2A and 2B - Form 3B: All transaction (income, expenditure and consumption) for the third and fourth weeks except what is collected in Forms 2A and 2B - Form 4: All transaction (expenditure and consumption) for last 6 months for Household expenditure on some selected item groups - Form 5: Cash income and receipts received by household and type of tenure. The survey questionnaire is provided as external resource.

Household Surveys performed in four villages selected from Oromia, Amhara and Southern Nations, Nationalities, and Peoples’ Region (SNNPR) following from the ‘Ethiopian Rural Household Survey’ (ERHS) conducted in 2004.It contains detailed data on household consumption and expenditures, assets, income, agricultural activities, land allocation, demographic characteristics, and other variables. From September 2011 to January 2012 another survey of 221 households was conducted in three major regions of central and southern Ethiopia. At the time of this latest survey effort the most recent ERHS survey data available was from 2004. The selection of respondents, determination of sample size, and apportionment of the sample were based on a proportional sampling technique.In addition to addressing important questions from the ERHS survey data, the field survey was designed to generate detailed information on household biomass energy production and consumption practices; as well as farming activities; labour and land allocation; economic and demographic characteristics; and expenditures on food, non-food items, and energy. The 2011 survey effort collected detailed household biomass energy use data. The measurement of household biomass energy use was obtained in traditional units and later converted into kilograms. The conversion factors for each of the biomass were collected from the closest urban centre of each of the study areas. Information obtained on household biomass energy use was collected for a time period of one week before the survey was conducted. It was then aggregated into annual figures, although household biomass energy use may vary seasonally. Quality/Lineage: The data was collected by qualified enumerators who had participated in previous ERHS survey. In addition to myself I recruited assistant supervisor to check the accuracy and quality of data on daily basis and followup interview process closely. Before the survey commenced a pilot survey was conducted in each of the study areas to identify the different types of energy households are using and other critical variables of interest for the research. This information was used to revise and improve questionnaire. Moreover, a one day in-depth training was given to enumerators and assistant supervisor to enrich their deeper understanding of each the question in the survey and to further improve questionnaire from their earlier experiences in those villages. Purpose: Over 90% of Ethiopian rural population rely on biomass energy. However, biomass energy utilization is linked to household livelihood as in rural households produce and consume biomass energy simultaneously with other (on and off-farm)activities. With the rampant rate of deforestation that Ethiopia is facing it is important to investigate the effect of deforestation or fuelwood scarcity which is assumed affect household welfare through influence on wage and price. In light of this, the survey effort collected information on household use of biomass energy sources, expenditure and labour allocation choices and amount of labour time used for each activities.This helped me to investigate the effect of fuelwood scarcity on household welfare from three aspects: labour allocation decision, energy expenditure and fuel choice and biomass energy consumption behavior to better understand the related linkage of household production and utilization of biomass with livelihoods or food security.

Abstract

The potential impacts of the COVID-19 pandemic in Ethiopia are expected to be severe on Ethiopian households' welfare. To monitor these impacts on households, the team selected a subsample of households that had been interviewed for the Living Standards Measurement Study (LSMS) in 2019, covering urban and rural areas in all regions of Ethiopia. The 15-minute questionnaire covers a series of topics, such as knowledge of COVID and mitigation measures, access to routine healthcare as public health systems are increasingly under stress, access to educational activities during school closures, employment dynamics, household income and livelihood, income loss and coping strategies, and external assistance.

The survey is implemented using Computer Assisted Telephone Interviewing, using a modular approach, which allows for modules to be dropped and/or added in different waves of the survey. Survey data collection started at the end of April 2020 and households are called back every three to four weeks for a total of seven survey rounds to track the impact of the pandemic as it unfolds and inform government action. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

The sample of households was drawn from the sample of households interviewed in the 2018/2019 round of the Ethiopia Socioeconomic Survey (ESS). The extensive information collected in the ESS, less than one year prior to the pandemic, provides a rich set of background information on the COVID-19 High Frequency Phone Survey of households which can be leveraged to assess the differential impacts of the pandemic in the country.

Geographic coverage

National coverage - rural and urban

Analysis unit

Individual and household

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the HFPS-HH is a subsample of the 2018/19 Ethiopia Socioeconomic Survey (ESS). The ESS is built on a nationally and regionally representative sample of households in Ethiopia. ESS 2018/19 interviewed 6,770 households in urban and rural areas. In the ESS interview, households were asked to provide phone numbers either their own or that of a reference household (i.e. friends or neighbors) so that they can be contacted in the follow-up ESS surveys should they move from their sampled location. At least one valid phone number was obtained for 5,374 households (4,626 owning a phone and 995 with a reference phone number). These households established the sampling frame for the HFPS-HH.

To obtain representative strata at the national, urban, and rural level, the target sample size for the HFPS-HH is 3,300 households; 1,300 in rural and 2,000 households in urban areas. In rural areas, we attempt to call all phone numbers included in the ESS as only 1,413 households owned phones and another 771 households provided reference phone numbers. In urban areas, 3,213 households owned a phone and 224 households provided reference phone numbers. To account for non-response and attrition all the 5,374 households were called in round 1 of the HFPS-HH.

The total number of completed interviews in round one is 3,249 households (978 in rural areas, 2,271 in urban areas). The total number of completed interviews in round two is 3,107 households (940 in rural areas, 2,167 in urban areas). The total number of completed interviews in round three is 3,058 households (934 in rural areas, 2,124 in urban areas). The total number of completed interviews in round four is 2,878 households (838 in rural areas, 2,040 in urban areas). The total number of completed interviews in round five is 2,770 households (775 in rural areas, 1,995 in urban areas). The total number of completed interviews in round six is 2,704 households (760 in rural areas, 1,944 in urban areas). The total number of completed interviews in round seven is 2,537 households (716 in rural areas, 1,1821 in urban areas). The total number of completed interviews in round eight is 2,222 households (576 in rural areas, 1,646 in urban areas). The total number of completed interviews in round nine is 2,077 households (553 in rural areas, 1,524 in urban areas). The total number of completed interviews in round ten is 2,178 households (537 in rural areas, 1,641 in urban areas). The total number of completed interviews in round eleven is 1,982 households (442 in rural areas, 1,540 in urban areas). The total number of completed interviews in round twelve is 888 households (204 in rural areas, 684 in urban areas). The total number of completed interviews in round thirteen is 2,876 households (955 in rural areas, 1,921 in urban areas). The total number of completed interviews in round fourteen is 2,509 households (765 in rural areas, 1,744 in urban areas). The total number of completed interviews in round fifteen is 2,521 households (823 in rural areas, 1,698 in urban areas). The total number of completed interviews in round sixteen is 2,336 households. The total number of completed interviews in round seventeen is 2,357 households. The total number of completed interviews in round eighteen is 2,237 households (701 in rural areas, 1,536 in urban areas). The total number of completed interviews in round nineteen is 2,566 households (806 in rural areas, 1,760 in urban areas).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires were administered to all the households in the sample. The questionnaires consisted of the following sections:

Baseline (Round 1) - Household Identification - Interview Information - Household Roster - Knowledge Regarding the Spread of Coronavirus - Behavior and Social Distancing - Access to Basic Services - Employment - Income Loss and Coping - Food Security - Aid and Support/ Social Safety Nets

Round 2 - Household Identification - Household Roster - Access to Basic Services - Employment - Income Loss and Coping - Food Security - Aid and Support/ Social Safety Nets

Round 3 - Household Identification - Household Roster - Behavior and social distancing - Access to Basic Services - Employment - Income Loss and Coping - Food Security - Agriculture - Aid and Support/ Social Safety Nets

Round 4 - Household Identification - Household Roster - Access to Basic Services - Employment - Income Loss and Coping - Food Security - Agriculture - Aid and Support/ Social Safety Nets - Locusts - WASH

Round 5 - Household Identification - Household Roster - Access to Basic Services - Employment - Income Loss and Coping - Aid and Support/ Social Safety Nets - Agriculture - Livestock

Round 6 - Household Identification - Household Roster - Behavior and Social Distancing - Access to Basic Services - Employment - Income Loss and Coping - Aid and Support/ Social Safety Nets - Agriculture - Locusts

Round 7 - Household Identification - Household Roster - Behavior and Social Distancing - Access to Basic Services - Employment - Income Loss and Coping - Aid and Support/ Social Safety Nets - Agriculture - Locusts

Round 8 - Household Identification - Household Roster - Access to Basic Services - Employment - Education and Childcaring - Credit - Migration - Return Migration

Round 9 - Household Identification - Household Roster Update - Access to Basic Services - Employment - Aid and Support/ Social Safety Nets - Agriculture - WASH

Round 10 - Household Identification - Household Roster Update - Access to Basic Services - Employment

Round 11 - Household Identification - Household Roster Update - Access to Basic Services - Employment - Education and Childcaring - Food Insecurity Experience Scale - SWIFT

Round 12 - Household Identification - Household Roster Update - Youth Aspirations and Employment

Round 13 - Household Identification - Household Roster Update - Access to Health Services - Employment - Food Prices

Round 14 - Household Identification - Household Roster Update - Access to Health Services - COVID-19 Vaccine - Employment - Economic Sentiments - Food Prices - Agriculture

Round 15 - Household Identification - Household Roster Update - Access to Health Services - Economic Sentiments - Food Insecurity Experience Scale - Food Prices

Round 16 - Household Identification - Household Roster Update - Access to Health Services - Employment and Non-farm Enterprises - Food and Non-food prices - Shocks and Coping Strategies - Subjective Welfare

Round 17 - Household Identification - Household Roster Update - Access to Health Services for Individual Household Members (Sample A) - Access to Health Services for Households (Sample B) - Food and Non-food prices - Economic Sentiments
- Food Insecurity Experience Scale

Round 18 - Household Identification - Household Roster Update - Access to Health Services for Individual Household Members - Food and Non-food prices - Economic Sentiments (Sample B) - Food Insecurity Experience Scale (Sample A)

Round 19 - Household Identification - Household's Residential Location Verification - Household Roster Update - Food and Non-food Prices - Agriculture Crop - Agriculture Livestock

Cleaning operations

DATA CLEANING At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. The details are as follows.

Variable naming and labeling: • Variable names were changed to reflect the lowercase question name in the paper survey copy, and a word or two related to the question.

• Variables were labeled

This dataset was generated as part of a research project that aimed to identify relevant gender considerations associated with wheat varietal development and the modifications required in wheat seed value chains in order to ensure inclusive diffusion of varieties and faster varietal turnover. Understanding the role that women play in agricultural production decisions is now widely considered as a pre-requisite to attain food security and alleviate poverty. There have been studies conducted on gender-sensitive breeding on a number of crops, however there are no evidence with respect to wheat in India. The role of gender-sensitive seed and information networks, which could facilitate the spread of varietal technologies ensuring social inclusiveness, is also rarely examined. The lack of control of women over the benefits from participation in value chains and gender discrimination in access to complementary inputs such as credit could be the reasons for not carrying out such analysis. However, when technology interventions do not capture gender-specific preferences for traits of varieties and dissemination networks, intra-household disparities in workloads and incomes persist or even worsen over time. This could also result in a lower rate of adoption by farm households. The study was conducted in the wheat growing tracts of Madhya Pradesh, with the the following research outputs expected. 1. Gender-specific farmer preferences for (a) wheat varietal traits and (b) attributes of seed and information networks are elicited. 2. Solutions to meet heterogeneous demand through inclusive delivery of improved wheat varieties among men and women farmers are identified and the associated transaction costs are estimated.

The empirical part of this study was conducted in three districts of Madhya Pradesh, India – Jabalpur, Mandla and Damoh – where one round of farm household survey and focus group discussions had been already completed one year ago.

In 2018, a first-round of farm household survey was conducted among 400 households in Madhya Pradesh (Dataset Persistent ID: hdl:11529/10548897). We interviewed both male and female heads (i.e., a total of 800 interviews). Questions were asked on women’s role in decision making and their involvement in farm-household activities. In addition to farm household surveys, 60 sex-specific focus group discussions were conducted with male and female farmers. The results revealed that the varietal turnover rate in wheat is significantly low in this region, with most farmers cultivating age-old varieties such as Lok 1.

The same farm households were interviewed in 2018, to form the current dataset. The aim was to better understand their preferences for wheat varietal traits (e.g., early maturing; suitability for chapatti making, drought tolerance etc.). Choice experiments and contingent valuation approaches will be used to elicit farmer preference for varietal attributes. The elicited preferences will be explained using data from the 2018 survey.

Gender equality is an indicator of sustainable development and also a means toward it. One of the Sustainable Development Goals (SDGs) set by United Nations is to achieve gender equality and empowerment of women and girls by 2030 (United Nations 2017). Empowerment of rural women is considered also as a necessary pre-requisite to attain food security and alleviate poverty in developing countries. While a number of studies address women empowerment as a developmental outcome, the deterministic role of rural women empowerment on agrarian development has not received sufficient research focus. On one hand, the quantitative empirical studies addressing technological change often limit the gender dimension to a binary variable on sex of the household head. The key roles and responsibilities of women members of the farm household, who are directly or indirectly involved in crop and livestock production, are overlooked by doing so. One the other hand, the in-depth qualitative case studies are not sufficiently broad (small sample size) to allow for generalization. Against this backdrop, the proposed study attempts to develop a mixed research methodology taking variables from quantitative household surveys and qualitative case studies for quickly and effectively capturing rural women involvement and empowerment and their ramifications on technological change and farmer livelihoods.

The empirical analysis will be based on Focus Group Discussions (FGDs) and household survey data, conducted in the second half of 2018 in Madhya Pradesh (India), where wheat is one of the main crops. The study frame was built in close collaboration with the CGIAR Gender and Agriculture Research Network, and also be a ‘pilot’ study for identifying and integrating gender variables in the adoption-impact studies in CRP WHEAT and MAIZE Programmes.

The overall objective of the project is to better understand the importance of women involvement in agriculture and women empowerment on technological change and rural livelihoods in India, focusing on impact heterogeneity and the role of different social institutions. There are two sets of research questions – first specific to the study area, and second more generic to the developing countries. The research questions specific to the study area are shown below. 1. Which of the individual / household / community characteristics are the key determinants of women empowerment in agriculture? 2. What role do gender plays on diffusion of varietal technologies and sustainable intensification practices in wheat? 3. What are the impacts of women empowerment and gender roles on household food insecurity? The more generic research questions are concerning (a) better technology targeting and (b) development of a variable set on gender to be used for quantitative data collection. The research questions framed in this connection are shown below. 4. How should the technology dissemination and targeting strategies change when the role, responsibilities, and preferences of women farmers are addressed? 5. Which are the easy to observe household attributes that could stand proxy for woman empowerment in quantitative studies on technological change?

The empirical part of this study will be based on data collected from three districts of Madhya Pradesh, India – Jabalpur, Mandla and Damoh. Madhya Pradesh is one of the states with largest wheat growing area (19% of wheat area in India) but with lower wheat productivity (2.85 tons) compared to other major producers (4.29 tons in Punjab and 3.98 tons in Haryana in 2014-15 season). While Mandla and Damoh are lower productive districts within the state, Jabalpur farmers experience moderately high wheat productivity. Mandla and Damoh are also categorized as the disadvantaged districts by Government of India. The selected districts contain the three GENNOVATE case-study communities.

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Comparison of expected births and deaths (based on endline survey) and reported births and deaths by HEWs, for 12-month validation periods, January, 2012 to March, 2013.

Description of household demographic and socioeconomic characteristics.

The study on Rural Energy and Household Forest Values under Varying Management Regimes was conducted in Ethiopia. The general objective of this survey is to study household behavior regarding sustainable land use. Households were selected from the main four regions of Ethiopia (Amhara, Oromia, SNNP, and Tigray) in 2009. The data was generated by researchers at the Ethiopian Development Research Institute and Gothenburg University.

The general objective of this survey is to study household behavior regarding communally managed forest resources. The datasets submitted here consist of different data files in Stata format. Each file corresponds to a section in the questionnaire.

The dataset contains six files from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) land cover transition data derived from LandSat satellite imagery for years 1986 and 2016. The files include data on household and plot features, prices of land use outputs, a diagonal block matrix of variables for mixed effects analysis, beliefs and opinions on land use change, and land cover transitions. The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and is representative of the region. It can be useful for studies on land use policies, environmental protection, and the drivers and impacts of land use land cover change in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17. Methods The ERSS/LSMS-ISA datasets are available in the public domain: Ethiopia Socioeconomic Survey (ESS) 2011-2012, Wave 1, http://dx.doi.org/10.48529/80xt-9m68; Ethiopia Socioeconomic Survey (ESS) 2013-2014, Wave 2, http://dx.doi.org/10.48529/mccp-y123; Ethiopia Socioeconomic Survey (ESS) 2015-2016, Wave 3, http://dx.doi.org/10.48529/ampf-7988. The data was accessed and filtered using the following criteria: saq01==1 & rural!=3, corresponding to selected households in the Tigray region engaged in farming activities in rural areas and small towns. Moreover, anyone can access LanSat imageries at https://earthexplorer.usgs.gov/

Percentage distribution of household DD according to the characteristics of the study respondents and households, Ethiopia, 2018/19.